Why Graph Neural Networks are great for Fraud Detection

This makes them one of the most powerful techniques in Machine Learning

Published in

AI Advances

3 min readNov 24, 2023

Graph Neural Networks are a mainstay in Fraud Detection. Let’s look into why.

Fraudsters tend to show strong correlations on a graph, because they have to share physical properties such as personal identities, phone devices, Wi-Fi routers, delivery addresses, and so on, to reduce cost and maximise revenue. An example of such strong correlations is shown in Figure below, where the entities on the graph are densely connected, and the known fraudsters are highlighted in red. Those strong correlations on the graph are the key reasons that make the graph based approach a sustainable foundation for various fraud detection tasks.

Graphs Neural Networks also come with other advantages over traditional techniques. They don’t require a lot of labeled data or feature engineering (which holds back Decision Trees), have better explainability than regular deep learning, and adapt to newer inputs (something rule based systems can’t do)

All of these make Graph Neural Networks an extremely powerful tool for Fraud Detection. To recap- graphs can be used to cluster and group people around behaviors that correlate strongly with fraud, can adapt to new fraud patterns well, and improve transparency of your AI Systems.

Learning about Graph Neural Networks is a must for anyone looking to boost their machine learning skills.

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Images From “Graph for fraud detection” by Grab- https://engineering.grab.com/graph-for-fraud-detection?fbclid=IwAR0f7L5p1A2RnfFlfqzDjWNJ-mwNUmDYG23GzW4PSh4jq6KWn2vU8SG4cZw

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